Reference Policy

Reference policies, encompassing the selection and utilization of prior knowledge or models, are crucial in various applications, from autonomous systems control to large language model training. Current research focuses on optimizing reference selection for improved performance, exploring techniques like neural collaborative filtering and model-based control strategies to manage and leverage this information effectively. Understanding and optimizing reference policies is vital for enhancing the accuracy, safety, and efficiency of diverse systems, ranging from aircraft navigation to automated content generation.